CS231n-2017-Summary and stanford-cs231n-assignments-2020
About CS231n-2017-Summary
mbadry1/CS231n-2017-Summary
After watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it. I've skipped some contents in some lectures as it wasn't important to me.
This document summarizes the key concepts from Stanford's CS231n 2017 course on Convolutional Neural Networks for Visual Recognition. It provides an overview of image classification, neural networks, loss functions, and optimization techniques. This resource is ideal for anyone looking for a condensed explanation of deep learning fundamentals applied to computer vision tasks, particularly those who have viewed the lectures and want a review.
About stanford-cs231n-assignments-2020
amanchadha/stanford-cs231n-assignments-2020
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).
This collection of assignments helps you learn to build AI systems that can 'see' and interpret images, similar to how human brains process visual information. You'll work with images and develop models that can classify objects, understand visual styles, or even generate new images. This is ideal for students or engineers new to computer vision and deep learning who want to build foundational skills.
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